Text-to-image diffusion models excel in generating photo-realistic images but are hampered by slow processing times. Training-free retrieval-based acceleration methods, which leverage pre-generated “trajectories,” have been introduced to address this. Yet, these methods often lack diversity and fidelity as they depend heavily on similarities to stored prompts. To address this, we present ReCon (Retrieving Concepts), an innovative retrieval-based diffusion acceleration method that extracts visual “concepts” from prompts, forming a knowledge base that facilitates the creation of adaptable trajectories. Consequently, ReCon surpasses existing retrieval-based methods, producing high-fidelity images and reducing required Neural Function Evaluations (NFEs) by up to 40%. Extensive testing on MS-COCO, Pick-a-pick, and DiffusionDB datasets confirms that ReCon consistently outperforms established methods across multiple metrics such as Pick Score, CLIP Score, and Aesthetics Score. A user study further indicates that 76% of images generated by ReCon are rated as the highest fidelity, outperforming two competing methods, a purely text-based retrieval and a noise similarity-based retrieval. Project URL: https://stevencylu.github.io/ReCon .